一个求解退化约束优化问题全局和超线性收敛的可行SQP算法(英文)

提出了一个求解退化约束优化问题的可行SQP算法.在该算法的每一次迭代,通过求解一个二次规划子问题得到可行下降方向,为克服Maratos效应,高阶修正方向通过求解另一个二次规划子问题得到.在合适的条件下,证明了该算法的全局收敛性和超线性收敛速度.最后给出了一些初步的数值结果.

A Globally and Superliearly Convergent Feasible SQP Algorithm for Degenerate Constrained Optimization Problem

In this paper, A FSQP algorithm for degenerate inequality constraints optimization problems is proposed. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving a quadratic programming subproblem. To overcome the Maratos effect, a higher-order correction direction is obtained by solving another quadratic programming subproblem. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions. Finally, some preliminary numerical results are reported.